Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy

Author:

Sathish Kaveripakam1,Chinthaginjala Ravikumar1ORCID,Kim Wooseong2ORCID,Rajesh Anbazhagan3ORCID,Corchado Juan M.456ORCID,Abbas Mohamed7ORCID

Affiliation:

1. School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India

2. Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea

3. School of Electrical and Electronics Engineering, SASTRA University, Thanjavur 613401, India

4. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain

5. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain

6. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan

7. Electrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia

Abstract

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object’s position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.

Funder

King Khalid University

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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